Estimation of metabolic syndrome heritability in three large populations including full pedigree and genomic information

Francesca Graziano, Ginevra Biino, Maria Teresa Bonati, Benjamin M Neale, Ron Do, Maria Pina Concas, Simona Vaccargiu, Mario Pirastu, Oscar Terradura-Vagnarelli, Massimo Cirillo, Martino Laurenzi, Mario Mancini, Alberto Zanchetti, Mario Grassi

Research output: Contribution to journalArticlepeer-review

Abstract

Metabolic syndrome is a complex human disorder characterized by a cluster of conditions (increased blood pressure, hyperglycemia, excessive body fat around the waist, and abnormal cholesterol or triglyceride levels). Any of these conditions increases the risk of serious disorders such as diabetes or cardiovascular disease. Currently, the degree of genetic regulation of this syndrome is under debate and partially unknown. The principal aim of this study was to estimate the genetic component and the common environmental effects in different populations using full pedigree and genomic information. We used three large populations (Gubbio, ARIC, and Ogliastra cohorts) to estimate the heritability of metabolic syndrome. Due to both pedigree and genotyped data, different approaches were applied to summarize relatedness conditions. Linear mixed models (LLM) using average information restricted maximum likelihood (AIREML) algorithm were applied to partition the variances and estimate heritability (h(2)) and common sib-household effect (c(2)). Globally, results obtained from pedigree information showed a significant heritability (h(2): 0.286 and 0.271 in Gubbio and Ogliastra, respectively), whereas a lower, but still significant heritability was found using SNPs data ([Formula: see text]: 0.167 and 0.254 in ARIC and Ogliastra). The remaining heritability between h(2) and [Formula: see text] ranged between 0.031 and 0.237. Finally, the common environmental c(2) in Gubbio and Ogliastra were also significant accounting for about 11% of the phenotypic variance. Availability of different kinds of populations and data helped us to better understand what happened when heritability of metabolic syndrome is estimated and account for different possible confounding. Furthermore, the opportunity of comparing different results provided more precise and less biased estimation of heritability.
Original languageEnglish
Pages (from-to)739-748
Number of pages10
JournalHuman Genetics
Volume138
Issue number7
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Cohort Studies
  • Female
  • *Genetic Predisposition to Disease
  • Genetics, Population/*methods
  • *Genome, Human
  • *Genome-Wide Association Study
  • Genomics/*methods
  • Genotype
  • Humans
  • Male
  • Metabolic Syndrome/*genetics
  • Models, Genetic
  • Pedigree
  • *Polymorphism, Single Nucleotide

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